This invention relates in general to wireless telecommunications networks and applications and, in particular, to a method and system of detecting trends in a telecommunications network. More particularly, the invention relates to methods of accessing, searching and correlating data stored in the operations and maintenance databases of a variety of network systems for performing optimization, maintenance and troubleshooting on a communications network.
Without limiting the scope of the invention, its background is described in connection with detecting trends in a telecommunications network utilizing a data mining tool, as an example.
Present-day mobile telephony has spurred rapid technological advances in both wireless and wireline communications. The wireless industry, in particular, is a rapidly growing industry, with advances, improvements, and technological breakthroughs occurring on an almost daily basis. Many mobile or wireless telecommunications systems, among them the European GSM-system and third generation systems (e.g., cdma2000), have passed through several advancements and development phases, and system designers are now concentrating on further improvements to such systems, including system refinements and the introduction of optional subscriber services.
Most telecommunication networks include a Switching System (SS) and a Base Station System (BSS). Each of these systems contain a number of functional units which process information and carry out operations of a functional telecommunications network. The functional units themselves may be implemented utilizing various telecommunication hardware devices.
Communicably coupled to the SS and BSC for each telecommunications network is an Operations and Maintenance Center (OMC). That is, the SS and BSC contain equipment used to connect the OMC to the network. Those skilled in the art will appreciate that OMC may be referred to as OSS in a Global System for Mobile (GSM) Communication system, or RANOS in a WCDMA network system. Those skilled in the art will also appreciate that each vendor supports a standard database structure for their own database systems. The OMC database systems of the various networks are configured to collect, maintain and store data parameters indicative of the performance of a corresponding network. Data parameters include, for example, BTS power levels, timing advance values, Radio Disturbance Recordings (RDR), group call cell positioning, and location services.
Various methods and systems currently exist for detecting trends among data parameters within wireless telecommunication networks. One of the most widely utilized methods involves manually searching Operations and Maintenance (OandM) database systems. The results from the various manual searches are then analyzed to find correlations or dependencies between the data parameters. Such tools are helpful in detecting trends in a network in order to perform optimization, maintenance and troubleshooting on the network. However, taken together, the manual search results are often inaccurate because of the dependence on skilled personnel. That is, the chances for human error are increased with this method. Furthermore, the problem becomes even more complex in a multi-vendor network with a plurality of OandM database systems in the network. In such a situation, skilled personnel are required and time consumption becomes a factor in manually searching all databases.
Vendor specific tools also exist, such as vendor specific network optimization products. This method is, however, vendor dependent. For example, in a multi-vendor system where a service provider may have 1,000 cells and another service provider may have 1,000 cells, and so on, data parameters of the various cells may not be correlated. That is, it is not possible to analyze the configuration of all cells to see if they are equal. Therefore, in order to verify that cell configurations in a different system with a plurality of vendors are equal, a method is needed which is vendor independent.
Another method utilized to detect trends or patterns among data involves utilizing Bots. The word xe2x80x9cBotxe2x80x9d is short for robot, which is derived from the Czech word robota meaning work. A Bot is a software tool for digging through data. In application, a Bot is given directions and returns results. In the Internet industry, there are at least three well known types of Bots: Indexing Bots, Shopping Bots and Data Mining Bots. Data Mining Bots is the process of finding patterns in enormous amounts of data. Because data mining often requires a series of searches, Bots can save labor as they persist in a search, refining as they go along. Data mining, for example, can help retail companies find customers with common interests.
In short, the prior art methods of detecting trends in a network are generally unsuitable for today""s modern wireless network. What is needed is a vendor independent and automated method of detecting trends in a network that utilizes a data mining tool, or bots. A means of accessing vendor database systems and searching for correlations and dependencies among an enormous amount of telecommunications data efficiently is needed in order to consume less time and allow for accuracy. That is, obtaining accurate patterns found among data will assist in optimization, maintenance and troubleshooting ofnetworks.
The present invention provides a method and system for detecting radio network trends in a telecommunications network. With the present invention, the network operator, for example, can identify correlations and dependencies which can then be used in optimizing, maintaining and troubleshooting the network.
Disclosed in one embodiment is a method of detecting radio network trends in a telecommunications network. The method comprises the step of accessing the operations and maintenance databases of the network via a data mining tool. Initially, the scope of the search to be accomplished by the data mining tool via parameters of the databases to be searched is specified.
The method also comprises the step of searching through the databases to find correlations and dependencies. That is, the data mining tool parses through the databases in its attempt to recognize patterns across the databases searched. If such patterns are identified, then the patterns are correlated within a database and across all databases in order to generate an output. Furthermore, the data mining tool is equipped to parse through standard formatted databases, as well as System Query Language (SQL) type structures in order to retrieve and maintain data arranged within.
The method further comprises the step of reporting the correlations and dependencies. Once the information sought has been retrieved, the data mining tool is configured to translate the vendor information found in the database systems to generic form. An output is then generated for a network engineer, for example, to use in determining areas of improvement or for analyzing the performance of the corresponding networks.
Technical advantages of the present invention include an automated method of detecting trends in the network compared to the manual searching approach presently utilized. Correlations and dependencies are identified in the shortest amount of time and by use of a data mining tool currently used in the Internet industry.
Other technical advantages include more accurate identification and analysis of network trends and patterns. The method and system of the present invention utilize data contained in vendor OandM database systems. As such, the trend analysis takes into account all data in one or more database systems within and across the network due to vendor independence.